{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据文档类别型特征有season,mnth,weathersit,weekday:season有一年四季，月份有1到12月，星期有1到7,天气有1，2，3，4四个取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print ('\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print (data[col].value_counts())\n",
    "    data[col] = data[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于数值型特征，直接打印分布直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000242A244D240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000242A26ED898>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000242A2717DD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000242A2745390>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "data[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，和这些都有一定关系，接下来查看年份关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x242a28637b8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=data[['yr', 'cnt']],x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "年份不同分布差异也不同，2012年比2011年多很多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'dayly distribution of counts')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "data['date'] = pd.to_datetime(data['dteday'])\n",
    "data['dayofyear'] = data[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sns.pointplot(data=data[['dayofyear',\n",
    "                           'cnt',\n",
    "                           'yr']],\n",
    "             x='dayofyear',y='cnt',\n",
    "             hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "日期转换成天数，同样发现2012年同一日期基本上要比2011年高（个别比较低）而且都是开始的和结束的数量少，中间的比较多，应该时和季节或月份有关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=data[['season',\n",
    "                       'cnt']],\n",
    "           x=\"season\",y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "春天和冬天人数少一些，夏天和秋天人数比较多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x242a34d2080>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(ncols=2)\n",
    "sns.barplot(data=data,x='holiday',y='cnt',ax=ax1)\n",
    "sns.barplot(data=data,x='workingday',y='cnt',ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假日里参加的人数不参加的人数比较多，工作日参加的人数比较多。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x242a381c320>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = data[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "                  \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sns.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "温度与体感温度强相关，达到了0.99，需要加入正则性"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
